A Known Pathogenic Variant in the Essential Mitochondrial Translation Gene RMND1 Causes a Perrault-Like Syndrome with Renal Defects

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Total hip arthroplasty surgical approach does not alter postoperative gait mechanics one year after surgery

Objective: To investigate the differences in gait biomechanics on the basis of surgical approach 1 year after surgery. Design: This was a descriptive laboratory study to investigate the side-to-side differences in walking mechanics at a self-selected walking speed as well as a functional assessment 1year after total hip arthroplasty (THA). Temporospatial, kinetic, and kinematic data as well as functional outcomes were collected. Two-way analysis of variance was used to assess for between-group differences and limb-to-limb asymmetries. Setting: A controlled laboratory study. Participants: This study examined 35 patients with primary, unilateral THA. The THA surgical approaches that were used in these patients included 12 direct lateral, 18 posterior, and 11 anterolateral. All the patients were assessed 1 year after THA. Patients were excluded from the study if they had contralateral hip pain or pathology, or any prior lower extremity total joint replacements. Main Outcome Measurements: Three-dimensional lower extremity kinematics and kinetics as well as spatiotemporal variables were collected. In addition, a series of physical performance measures were collected. Results: No main effects for the physical performance measures or biomechanical variables were observed among the approach groups. Significant limb-to-limb asymmetries were observed among all the patients, with decreased sagittal plane range of motion, peak extension, and peak vertical ground reaction forces on the operative side. Conclusion: The results of this study indicated that no significant differences existed among the different surgical approach groups for any study variable. However, 1 year after THA, the patients demonstrated asymmetric gait patterns regardless of surgical approach, which indicated the potential need for continued intervention through physical therapy to regain normal side-to-side symmetry after THA. © 2014 American Academy of Physical Medicine and Rehabilitation

The role of asymmetric interactions on the effect of habitat destruction in mutualistic networks

Plant-pollinator mutualistic networks are asymmetric in their interactions: specialist plants are pollinated by generalist animals, while generalist plants are pollinated by a broad involving specialists and generalists. It has been suggested that this asymmetric ---or disassortative--- assemblage could play an important role in determining the equal susceptibility of specialist and generalist plants under habitat destruction. At the core of the argument lies the observation that specialist plants, otherwise candidates to extinction, could cope with the disruption thanks to their interaction with generalist pollinators. We present a theoretical framework that supports this thesis. We analyze a dynamical model of a system of mutualistic plants and pollinators, subject to the destruction of their habitat. We analyze and compare two families of interaction topologies, ranging from highly assortative to highly disassortative ones, as well as real pollination networks. We found that several features observed in natural systems are predicted by the mathematical model. First, there is a tendency to increase the asymmetry of the network as a result of the extinctions. Second, an entropy measure of the differential susceptibility to extinction of specialist and generalist species show that they tend to balance when the network is disassortative. Finally, the disappearance of links in the network, as a result of extinctions, shows that specialist plants preserve more connections than the corresponding plants in an assortative system, enabling them to resist the disruption.Comment: 14 pages, 7 figure

Spectral plots and the representation and interpretation of biological data

It is basic question in biology and other fields to identify the char- acteristic properties that on one hand are shared by structures from a particular realm, like gene regulation, protein-protein interaction or neu- ral networks or foodwebs, and that on the other hand distinguish them from other structures. We introduce and apply a general method, based on the spectrum of the normalized graph Laplacian, that yields repre- sentations, the spectral plots, that allow us to find and visualize such properties systematically. We present such visualizations for a wide range of biological networks and compare them with those for networks derived from theoretical schemes. The differences that we find are quite striking and suggest that the search for universal properties of biological networks should be complemented by an understanding of more specific features of biological organization principles at different scales.Comment: 15 pages, 7 figure

Colored Motifs Reveal Computational Building Blocks in the C. elegans Brain

Background: Complex networks can often be decomposed into less complex sub-networks whose structures can give hints about the functional organization of the network as a whole. However, these structural motifs can only tell one part of the functional story because in this analysis each node and edge is treated on an equal footing. In real networks, two motifs that are topologically identical but whose nodes perform very different functions will play very different roles in the network. Methodology/Principal Findings: Here, we combine structural information derived from the topology of the neuronal network of the nematode C. elegans with information about the biological function of these nodes, thus coloring nodes by function. We discover that particular colorations of motifs are significantly more abundant in the worm brain than expected by chance, and have particular computational functions that emphasize the feed-forward structure of information processing in the network, while evading feedback loops. Interneurons are strongly over-represented among the common motifs, supporting the notion that these motifs process and transduce the information from the sensor neurons towards the muscles. Some of the most common motifs identified in the search for significant colored motifs play a crucial role in the system of neurons controlling the worm's locomotion. Conclusions/Significance: The analysis of complex networks in terms of colored motifs combines two independent data sets to generate insight about these networks that cannot be obtained with either data set alone. The method is general and should allow a decomposition of any complex networks into its functional (rather than topological) motifs as long as both wiring and functional information is available

Recruitment of ethnic minority patients to a cardiac rehabilitation trial: The Birmingham Rehabilitation Uptake Maximisation (BRUM) study [ISRCTN72884263]

Background: Concerns have been raised about low participation rates of people from minority ethnic groups in clinical trials. However, the evidence is unclear as many studies do not report the ethnicity of participants and there is insufficient information about the reasons for ineligibility by ethnic group. Where there are data, there remains the key question as to whether ethnic minorities more likely to be ineligible (e.g. due to language) or decline to participate. We have addressed these questions in relation to the Birmingham Rehabilitation Uptake Maximisation (BRUM) study, a randomized controlled trial (RCT) comparing a home-based with a hospital-based cardiac rehabilitation programme in a multi-ethnic population in the UK. Methods: Analysis of the ethnicity, age and sex of presenting and recruited subjects for a trial of cardiac rehabilitation in the West-Midlands, UK. Participants: 1997 patients presenting post-myocardial infarction, percutaneous transluminal coronary angioplasty or coronary artery bypass graft surgery. Data collected: exclusion rates, reasons for exclusion and reasons for declining to participate in the trial by ethnic group. Results: Significantly more patients of South Asian ethnicity were excluded (52% of 'South Asian' v 36% 'White European' and 36% 'Other', p < 0.001). This difference in eligibility was primarily due to exclusion on the basis of language (i.e. the inability to speak English or Punjabi). Of those eligible, similar proportions were recruited from the different ethnic groups (white, South Asian and other). There was a marked difference in eligibility between people of Indian, Pakistani or Bangladeshi origin

Multiple Imputation Ensembles (MIE) for dealing with missing data

Missing data is a significant issue in many real-world datasets, yet there are no robust methods for dealing with it appropriately. In this paper, we propose a robust approach to dealing with missing data in classification problems: Multiple Imputation Ensembles (MIE). Our method integrates two approaches: multiple imputation and ensemble methods and compares two types of ensembles: bagging and stacking. We also propose a robust experimental set-up using 20 benchmark datasets from the UCI machine learning repository. For each dataset, we introduce increasing amounts of data Missing Completely at Random. Firstly, we use a number of single/multiple imputation methods to recover the missing values and then ensemble a number of different classifiers built on the imputed data. We assess the quality of the imputation by using dissimilarity measures. We also evaluate the MIE performance by comparing classification accuracy on the complete and imputed data. Furthermore, we use the accuracy of simple imputation as a benchmark for comparison. We find that our proposed approach combining multiple imputation with ensemble techniques outperform others, particularly as missing data increases

A large-scale study of a poultry trading network in Bangladesh: implications for control and surveillance of avian influenza viruses

Since its first report in 2007, avian influenza (AI) has been endemic in Bangladesh. While live poultry marketing is widespread throughout the country and known to influence AI dissemination and persistence, trading patterns have not been described. The aim of this study is to assess poultry trading practices and features of the poultry trading networks which could promote AI spread, and their potential implications for disease control and surveillance. Data on poultry trading practices was collected from 849 poultry traders during a cross-sectional survey in 138 live bird markets (LBMs) across 17 different districts of Bangladesh. The quantity and origins of traded poultry were assessed for each poultry type in surveyed LBMs. The network of contacts between farms and LBMs resulting from commercial movements of live poultry was constructed to assess its connectivity and to identify the key premises influencing it

Fracturing ranked surfaces

Discretized landscapes can be mapped onto ranked surfaces, where every element (site or bond) has a unique rank associated with its corresponding relative height. By sequentially allocating these elements according to their ranks and systematically preventing the occupation of bridges, namely elements that, if occupied, would provide global connectivity, we disclose that bridges hide a new tricritical point at an occupation fraction $p=p_{c}$, where $p_{c}$ is the percolation threshold of random percolation. For any value of $p$ in the interval $p_{c}< p \leq 1$, our results show that the set of bridges has a fractal dimension $d_{BB} \approx 1.22$ in two dimensions. In the limit $p \rightarrow 1$, a self-similar fracture is revealed as a singly connected line that divides the system in two domains. We then unveil how several seemingly unrelated physical models tumble into the same universality class and also present results for higher dimensions

Null Models of Economic Networks: The Case of the World Trade Web

In all empirical-network studies, the observed properties of economic networks are informative only if compared with a well-defined null model that can quantitatively predict the behavior of such properties in constrained graphs. However, predictions of the available null-model methods can be derived analytically only under assumptions (e.g., sparseness of the network) that are unrealistic for most economic networks like the World Trade Web (WTW). In this paper we study the evolution of the WTW using a recently-proposed family of null network models. The method allows to analytically obtain the expected value of any network statistic across the ensemble of networks that preserve on average some local properties, and are otherwise fully random. We compare expected and observed properties of the WTW in the period 1950-2000, when either the expected number of trade partners or total country trade is kept fixed and equal to observed quantities. We show that, in the binary WTW, node-degree sequences are sufficient to explain higher-order network properties such as disassortativity and clustering-degree correlation, especially in the last part of the sample. Conversely, in the weighted WTW, the observed sequence of total country imports and exports are not sufficient to predict higher-order patterns of the WTW. We discuss some important implications of these findings for international-trade models.Comment: 39 pages, 46 figures, 2 table